Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures

10 May 2020

Papers citing "Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures"

21 / 21 papers shown

Title
PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferences Pingyi Huo Anusha Devulapally Hasan Al Maruf Minseo Park Krishnakumar Nair Meena Arunachalam Gulsum Gudukbay Akbulut M. Kandemir Vijaykrishnan Narayanan 34 0 0 25 Sep 2024
Efficient Tabular Data Preprocessing of ML Pipelines Yu Zhu Wenqi Jiang Gustavo Alonso LMTD 27 1 0 23 Sep 2024
Accelerating Recommender Model Training by Dynamically Skipping Stale Embeddings Yassaman Ebrahimzadeh Maboud Muhammad Adnan Divyat Mahajan Prashant J. Nair AI4TS 40 0 0 22 Mar 2024
[Experiments & Analysis] Evaluating the Feasibility of Sampling-Based Techniques for Training Multilayer Perceptrons Sana Ebrahimi Rishi Advani Abolfazl Asudeh 19 0 0 15 Jun 2023
Mem-Rec: Memory Efficient Recommendation System using Alternative Representation Gopu Krishna Jha Anthony Thomas Nilesh Jain Sameh Gobriel Tajana Rosing Ravi Iyer 53 2 0 12 May 2023
Hera: A Heterogeneity-Aware Multi-Tenant Inference Server for Personalized Recommendations Yujeong Choi John Kim Minsoo Rhu 21 1 0 23 Feb 2023
KAIROS: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources Baolin Li S. Samsi V. Gadepally Devesh Tiwari 22 11 0 12 Oct 2022
RIBBON: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances Baolin Li Rohan Basu Roy Tirthak Patel V. Gadepally K. Gettings Devesh Tiwari 32 25 0 23 Jul 2022
$The trade-offs of model size in large recommendation models : A 10000 $\times$ compressed criteo-tb DLRM model (100 GB parameters to mere 10MB)$ The trade-offs of model size in large recommendation models : A 10000 $\times$ compressed criteo-tb DLRM model (100 GB parameters to mere 10MB) Aditya Desai Anshumali Shrivastava AI4CE 20 3 0 21 Jul 2022
FSHMEM: Supporting Partitioned Global Address Space on FPGAs for Large-Scale Hardware Acceleration Infrastructure Y. F. Arthanto David Ojika Joo-Young Kim FedML 58 2 0 11 Jul 2022
Deep Learning Models on CPUs: A Methodology for Efficient Training Quchen Fu Ramesh Chukka Keith Achorn Thomas Atta-fosu Deepak R. Canchi Zhongwei Teng Jules White Douglas C. Schmidt 21 1 0 20 Jun 2022
FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems Rui Ma E. Georganas A. Heinecke Andrew Boutros Eriko Nurvitadhi GNN 24 12 0 22 Apr 2022
Heterogeneous Acceleration Pipeline for Recommendation System Training Muhammad Adnan Yassaman Ebrahimzadeh Maboud Divyat Mahajan Prashant J. Nair 31 18 0 11 Apr 2022
Building a Performance Model for Deep Learning Recommendation Model Training on GPUs Zhongyi Lin Louis Feng E. K. Ardestani Jaewon Lee J. Lundell Changkyu Kim A. Kejariwal John Douglas Owens 22 19 0 19 Jan 2022
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads E. Georganas Dhiraj D. Kalamkar Sasikanth Avancha Menachem Adelman Deepti Aggarwal ... Ramanarayan Mohanty Hans Pabst Brian Retford Barukh Ziv A. Heinecke 26 17 0 12 Apr 2021
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models Dheevatsa Mudigere Y. Hao Jianyu Huang Zhihao Jia Andrew Tulloch ... Ajit Mathews Lin Qiao M. Smelyanskiy Bill Jia Vijay Rao 34 149 0 12 Apr 2021
ECRM: Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding Kaige Liu J. Kosaian K. V. Rashmi 32 4 0 05 Apr 2021
CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery Kiwan Maeng Shivam Bharuka Isabel Gao M. C. Jeffrey V. Saraph ... Caroline Trippel Jiyan Yang Michael G. Rabbat Brandon Lucia Carole-Jean Wu OffRL 24 31 0 05 Nov 2020
Cross-Stack Workload Characterization of Deep Recommendation Systems Samuel Hsia Udit Gupta Mark Wilkening Carole-Jean Wu Gu-Yeon Wei David Brooks BDL GNN HAI 25 32 0 10 Oct 2020
Accelerating Recommender Systems via Hardware "scale-in" S. Krishna Ravi Krishna GNN LRM 24 6 0 11 Sep 2020
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems Maxim Naumov John Kim Dheevatsa Mudigere Srinivas Sridharan Xiaodong Wang ... Krishnakumar Nair Isabel Gao Bor-Yiing Su Jiyan Yang M. Smelyanskiy GNN 46 95 0 20 Mar 2020